基于随机森林与主成分分析的刀具磨损评估

doi:10.3901/JME.2017.21.181

机械工程学报 ›› 2017, Vol. 53 ›› Issue (21): 181-189.doi: 10.3901/JME.2017.21.181

扫码分享

基于随机森林与主成分分析的刀具磨损评估

赵帅¹, 黄亦翔¹, 王浩任¹, 刘成良¹, 刘晓², 梁鑫光²

1. 上海交通大学机械系统与振动国家重点实验室上海 200240;
2. 上海航天设备制造总厂上海 200245

收稿日期:2016-09-18 修回日期:2017-03-09 出版日期:2017-11-05 发布日期:2017-11-05
作者简介:黄亦翔(通信作者),男,1980年出生,博士,讲师。主要研究方向为机电系统智能维护、信号处理与模式识别、特征提取与降维。E-mail:huang.yixiang@sjtu.edu.cn
基金资助:
国家科技重大专项资助项目（2014ZX04015-021）。

Random Forest and Principle Components Analysis Based on Health Assessment Methodology for Tool Wear

ZHAO Shuai¹, HUANG Yixiang¹, WANG Haoren¹, LIU Chengliang¹, LIU Xiao², LIANG Xinguang²

1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240;
2. Shanghai Aerospace Equipment Manufacturer, Shanghai 200245

Received:2016-09-18 Revised:2017-03-09 Online:2017-11-05 Published:2017-11-05
Contact: 黄亦翔(通信作者),男,1980年出生,博士,讲师。主要研究方向为机电系统智能维护、信号处理与模式识别、特征提取与降维。E-mail:huang.yixiang@sjtu.edu.cn

摘要/Abstract

摘要： 刀具是数控机床的重要零部件，其性能直接影响着生产加工精度。为实现机床刀具磨损程度的在线分级评估，提出一种刀具磨损状态的评估方法，该方法结合随机森林与主成分分析模型，建立不同工况下主轴电机电流传感器信号样本与刀具磨损等级的非线性映射关系。在不同加工条件下进行刀具性能试验，采集主轴电机的电流信号和铣削加工参数。对信号利用小波包分解、时域统计、频域分析提取特征，利用随机森林得到刀具磨损的分级评估结果。该方法可有效解决样本不平衡的问题，与常见的组合分类方法AdaBoost所得结果相比，该模型能准确地反映刀具的磨损程度，鲁棒性更好。该方法仅利用数控机床内置传感器实现，无需改动机床结构，不影响主轴动态加工性能，可广泛应用于工业数控机床刀具的磨损评估。

关键词: 刀具磨损, 健康评估, 随机森林, 性能衰退, 主成分分析

Abstract: The cutting tool is a critical part of the CNC machine. Its performance directly affects the machining accuracy. A method to assess the wear status of the cutting tools is proposed based on the combination of random forest analysis and PCA to establish a nonlinear mapping relationship between the features of the spindle current signals and tool wear. The degrees of the tool wear are divided into several classifications. Experiments have been conducted by testing the tools of different machining conditions. Wavelet packet decomposition, time domain statistics and frequency domain analysis are performed on the signals for feature extraction. Then the random forest method is applied to evaluate and classify different tool status. Compared with the results from AdaBoost which is a common boost classification method, results show that the proposed model is more accurate and robust. The method can avoid the problem of imbalance samples. In addition, it can be realized on the build-in sensors of the industrial CNC machines so that there is no need to change the original structure design to avoid the potential interferences of the spindle's dynamic processing performance, which enables a wide industrial applications of the tool wear assessment for the CNC machines.

Key words: health assessment, performance degradation, principle components analysis, random forest, tool wear

中图分类号:

TH17

赵帅, 黄亦翔, 王浩任, 刘成良, 刘晓, 梁鑫光. 基于随机森林与主成分分析的刀具磨损评估[J]. 机械工程学报, 2017, 53(21): 181-189.

ZHAO Shuai, HUANG Yixiang, WANG Haoren, LIU Chengliang, LIU Xiao, LIANG Xinguang. Random Forest and Principle Components Analysis Based on Health Assessment Methodology for Tool Wear[J]. Journal of Mechanical Engineering, 2017, 53(21): 181-189.

参考文献

[1] LI Lihong, AN Qingbin. An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis[J]. Measurement, 2016, 79(2):44-52.
[2] SAMIK D, SURJYA K P, RANJAN S. Progressive tool flank wear monitoring by applying discrete wavelet transform on turned surface images[J]. Measurement, 2016, 77(1):388-401.
[3] MAURICIO E N, PAULO R A, HILDO G J, et al. Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics[J]. Expert Systems with Applications, 2015, 42(20):7026-7035.
[4] FARBOD A N, DURUL U, LAINE M. In-process tool flank wear estimation in machining gamma-prime strengthened alloys using Kalman filter[J]. Procedia Manufacturing, 2015, 1(1):696-707.
[5] MEHDI N, BARRY K F, BETH L Z, et al. Real-time tool wear monitoring in milling using a cutting condition independent method[J]. International Journal of Machine Tools and Manufacture, 2015, 89(2):1-13.
[6] AZMI A I. Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites[J]. Advances in Engineering Software, 2015, 82(4):53-64.
[7] WANG Guofeng, YANG Yinwei, XIE Qinglu, et al. Force based tool wear monitoring system for milling process based on relevance vector machine[J]. Advances in Engineering Software, 2014, 71(5):46-51.
[8] 陈保家,陈雪峰,李兵,等. Logistic回归模型在机床刀具可靠性评估中的应用[J]. 机械工程学报, 2011, 47(18):158-164. CHEN Baojia, CHEN Xuefeng, LI Bing, et al. Reliability estimation for cutting tool based on logistic regression model[J]. Journal of Mechanical Engineering, 2011, 47(18):158-164.
[9] KADU R S, AWARI G K, SAKHALE C N, et al. Formulation of mathematical model for the investigation of tool wears in boring machining operation on cast iron using carbide and CBN tools[J]. Procedia Materials Science, 2014, 6(1):1710-1724.
[10] YANG C Y, WU T Y. Diagnostics of gear deterioration using EEMD approach and PCA process[J]. Measurement, 2015, 61(2):75-87.
[11] LEO B. Random forests[J]. Machine Learning, 2001, 45(1):5-32.

[1]	段书用, 杨建华, 韩旭, 刘桂荣. 高维数据自适应降维方法[J]. 机械工程学报, 2024, 60(17): 283-296.
[2]	赖旭伟, 丁昆, 张楷, 黄锋飞, 郑庆, 李致萱, 丁国富. 基于可解释物理引导空间注意力改进的跨工艺参数立铣刀磨损辨识[J]. 机械工程学报, 2024, 60(12): 147-157.
[3]	武骥, 方雷超, 刘兴涛, 陈佳佳, 刘晓剑, 吕帮. 基于特征优化和随机森林算法的锂离子电池SOH估计[J]. 机械工程学报, 2024, 60(12): 335-343.
[4]	王润琼, 宋清华, 彭业振, 刘战强. 基于特征自适应融合和集成学习的高性能铣削刀具状态监测[J]. 机械工程学报, 2024, 60(1): 149-158.
[5]	都建标, 张强, 宗文俊. 硬脆及黑色金属材料的单点金刚石车削加工技术综述[J]. 机械工程学报, 2023, 59(7): 156-175.
[6]	黄景山, 刘国亮, 孙浩, 姚斌, 金林, 陈彬强, 曹新城, 王山城, 许浩, 蔡志钦. C60纳米粒子切削液对15Cr14Co12Mo5Ni2WA齿轮钢切削特性的影响研究[J]. 机械工程学报, 2023, 59(23): 358-371.
[7]	侯鑫尧, 倪强, 李学明, 黄庆. 列车牵引传动系统温度传感器实时故障诊断与容错估计^*[J]. 电气工程学报, 2023, 18(2): 269-276.
[8]	刘洪成, 袁德志, 朱锟鹏. 基于高斯过程潜力模型的刀具磨损预测[J]. 机械工程学报, 2023, 59(17): 310-324.
[9]	郑锐, 周一帆. 针对比例风险退化系统健康评估的迭代算法[J]. 机械工程学报, 2023, 59(10): 366-373.
[10]	段暕, 周宏娣, 刘智勇, 詹小斌, 梁健强, 史铁林. 基于改进PCANet模型的铣刀磨损预测方法研究[J]. 机械工程学报, 2023, 59(1): 278-285.
[11]	张晓英, 吴宏强, 舒子江, 王琨, 陈伟. 基于PCA和改进ABC-K聚类算法的双馈机组风电场等值建模^*[J]. 电气工程学报, 2023, 18(1): 201-210.
[12]	滕洪钊, 邓朝晖, 吕黎曙, 谷倩微, 刘涛, 卓荣锦. 多传感器信息融合的加工过程状态监测研究[J]. 机械工程学报, 2022, 58(6): 26-41.
[13]	黄文豪, 王军, 熊瑞, 陈欢, 王春. 温度和电压因素对超级电容性能衰退的影响机制及其老化建模研究[J]. 机械工程学报, 2022, 58(10): 235-244.
[14]	宋立业, 范抑伶, 王燚增. 基于KPCA与IHHO-LSSVM的电力变压器故障诊断方法研究^*[J]. 电气工程学报, 2022, 17(1): 95-103.
[15]	赵国龙, 夏宏军, 李亮, 信连甲, 何宁. 激光诱导可控氧化辅助微细铣削TiAl金属间化合物的研究[J]. 机械工程学报, 2021, 57(9): 254-263.

基于随机森林与主成分分析的刀具磨损评估

Random Forest and Principle Components Analysis Based on Health Assessment Methodology for Tool Wear

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价